INVESTIGATION OF SCALAR FORCING IN DIRECT NUMERICAL SIMULATIONS OF CLOUD TURBULENCE

2025 · doi:10.22541/au.174077749.99864898/v1
preprint OA: closed
Full text JSON View at publisher
Full text 2,149 characters · extracted from oa-doi-fallback · 2 sections · click to expand

Abstract

Direct numerical simulation (DNS) has become an indispensable tool to study turbulence-cloud-aerosol interactions. The DNS approach attempts to resolve the smallest scales of the flow, assuming homogeneous and isotropic turbulence. The governing equations for the flow of air, and two scalars-temperature, and water vapor mixing ratio, are solved in an Eulerian fashion while aerosol particles and cloud droplets are tracked using the Lagrangian method. To sustain turbulence in the scalar fields, forcing is necessary. This paper presents a DNS investigation of two scalar forcing mechanisms, in the spectral and physical space, respectively. We compare the forced and unforced scalar fields in terms of the standard deviation, the spectra, and the probability density functions. Our results show forcing leads to more fluctuations in the scalar fields and broadening of the probability distributions. Also, the rate of condensation decreases with scalar forcing. The scalar spectra for forced and unforced scalar fields are found to be different at small scales. Supplementary Material File (astfe-final paper-scalar.pdf) - Download - 721.22 KB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Keywords

Authors Funding Information Metrics & Citations Metrics Article Usage 160views 91downloads Citations Download citation Abdullah Al Muti Sharfuddin, Abdullah Al Muti, Foluso Ladeinde. INVESTIGATION OF SCALAR FORCING IN DIRECT NUMERICAL SIMULATIONS OF CLOUD TURBULENCE. Authorea. 28 February 2025. DOI: https://doi.org/10.22541/au.174077749.99864898/v1 DOI: https://doi.org/10.22541/au.174077749.99864898/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00